With the abundance of data made available through various means in general and the Internet and world-wide web (WWW) in particular, a need to understand likes and dislikes of users has become essential for on-line businesses.
Existing solutions provide several tools to identify users' preferences. Some prior art solutions actively require an input from users to specify their respective interests. However, profiles generated for users based on their inputs may be inaccurate, as the users tend to provide only their current interests, or otherwise only provide partial information due to privacy concerns.
Other existing solutions passively track the users' activity through particular web sites such as social networks. The disadvantage with such solutions is that typically limited information regarding the users is revealed, as users tend to provide only partial information due to privacy concerns. For example, users creating an account on Facebook® provide in most cases only the mandatory information required for the creation of the account. Additional information about such users may be collected over time, but may take significant amounts of time (i.e., gathered via multiple social media or blog posts over a time period of weeks or months) to be useful for accurate identification of user preferences.
Additionally, some existing solutions for determining user preferences attempt to identify and recommend content that is similar to content enjoyed by the user based on information noted by tags of the content such as, for example, subject matter, the entity that created the content, actors or actresses appearing in the content, and the like. Such solutions also face challenges based on lack of accurate information regarding what content the user has viewed and whether the user enjoyed such content.
It would therefore be advantageous to provide a solution that would overcome the deficiencies of the prior art.